Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant

Abstract

Vision based techniques have been widely used in precision farming especially to
control the application of chemical products on a specific area. This can help
minimizing the risk of soil and water pollution due to excessive application of
chemical products. Machine vision can be used to gather information while the
vehicle pulling the herbicide sprayer is in motion. This information can be processed,
analyzed, and transformed into inputs for a decisional algorithm that controls the
sprayer nozzle action in real-time. In this research, a vision system algorithm has
been developed to identify and locate base of young corn trees based upon robot
vision technology, pattern recognition techniques, and knowledge-based decision
theory. Results of studying color segmentation using machine-learning algorithm and
color space analysis is presented in this thesis. RGB (red, green, blue) color space
data points on an image are projected into HSV (hue, saturation, value) color space
to provide data points that are insensitive to the variations of illumination in outdoor
environment. Multi-layer perceptron (MLP) neural network trained using
backpropagation algorithm is used to segment the color image. The results of color segmentation show that the algorithm is able to segment the images reliably with less
appearance of small blobs. Morphological operation is applied to remove the small
blobs. Prior to localization of the base of young corn tree, skeletonizing operation is
performed to get the basic shape of the object. Another structure of MLP trained
using backpropagation algorithm is used to detect and locate the base of the young
corn tree using the skeleton of the segmented image. Prior to choosing the MLP
structures for both color segmentation and object detection, a number of experiments
have been conducted to find the best MLP structures that can give considerably good
recognition and classification rate with considerable amount of processing time
required. Results of the experiments to find the best MLP structures are presented
together with the discussion. The recognition rate is presented and compared with
another related research work, where the results show equal performance of both
algorithms. This shows that machine-learning algorithm such as MLP is a viable
method for color segmentation as well as object recognition.